Determining an item's dimensions is often necessary as part of a logistics process (e.g., shipping, storage, etc.). Physically measuring objects, however, is time consuming and may not result in accurate measurements. For example, in addition to human error, measurement errors may result when measuring irregularly shaped objects or when combining multiple objects into a single measurement. As a result, non-contact dimensioning systems have been developed to automate, or assist with, this measurement. These dimensioning systems sense an object's shape/size in three-dimensions (3D) and then use this 3D data to compute an estimate of an object's dimensions (e.g., length, width, height, etc.).
Accurate dimensioning is highly valued. For example, regulatory certification often demands highly accurate measurements when dimensioning is used for commercial transactions (e.g., determining shipping costs). Unfortunately, there are errors in the dimensions estimated by dimensioning system. One way to reduce these errors is to (i) constrain the size/shape of measured objects and (ii) place strict requirements on the measurement setup. These constraints, however, limit the flexibility of the dimensioning system and the speed at which a measurement may be taken. Therefore, a need exists for methods to reduce the errors associated with estimated dimensions returned from a dimensioning system.